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- W4361763926 abstract "Arabic nations share the same mother tongue, Arabic. However the used vernacular language is different, that, in turn, may vary from one region to another. In this paper, we aim to identify various dialects by performing text classification. We distinguish between Moroccan, Algerian, Tunisian, Egyptian, and Lebanese Arabic dialects denoted respectively MDA, ADA, TDA, EDA and LDA. Aside from explaining the collecting process of system resources, we identify linguistic specificities that characterize each dialect. We build models from the preprocessed text using a combination of the pretrained model, AraBERT; and Generative Adversarial Networks (GANS). The work establishes the foundation of a dialect identification system by gathering freely available corpora and reaching the state-of-the-art." @default.
- W4361763926 created "2023-04-04" @default.
- W4361763926 creator A5056314821 @default.
- W4361763926 date "2023-01-01" @default.
- W4361763926 modified "2023-09-27" @default.
- W4361763926 title "AraBERT with GANs for High Performance Fine-Grained Dialect Classification" @default.
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- W4361763926 doi "https://doi.org/10.1007/978-3-031-28387-1_15" @default.
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